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An Evidential Model of Distributed Reputation Management
 In Proceedings of First International Joint Conference on Autonomous Agents and Multiagent Systems
, 2002
"... For agents to function effectively in large and open networks, they must ensure that their correspondents, i.e., the agents they interact with, are trustworthy. Since no central authorities may exist, the only way agents can find trustworthy correspondents is by collaborating with others to identify ..."
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Cited by 125 (9 self)
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For agents to function effectively in large and open networks, they must ensure that their correspondents, i.e., the agents they interact with, are trustworthy. Since no central authorities may exist, the only way agents can find trustworthy correspondents is by collaborating with others to identify those whose past behavior has been untrustworthy. In other words, finding trustworthy correspondents reduces to the problem of distributed reputation management. Our approach adapts the mathematical theory of evidence to represent and propagate the ratings that agents give to their correspondents. When evaluating the trustworthiness of a correspondent, an agent combines its local evidence (based on direct prior interactions with the correspondent) with the testimonies of other agents regarding the same correspondent. We experimentally studied this approach to establish that some important properties of trust are captured by it.
Control of Selective Perception Using Bayes Nets and Decision Theory
, 1993
"... A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the neces ..."
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Cited by 100 (1 self)
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A selective vision system sequentially collects evidence to support a specified hypothesis about a scene, as long as the additional evidence is worth the effort of obtaining it. Efficiency comes from processing the scene only where necessary, to the level of detail necessary, and with only the necessary operators. Knowledge representation and sequential decisionmaking are central issues for selective vision, which takes advantage of prior knowledge of a domain's abstract and geometrical structure and models for the expected performance and cost of visual operators. The TEA1 selective vision system uses Bayes nets for representation and benefitcost analysis for control of visual and nonvisual actions. It is the highlevel control for an active vision system, enabling purposive behavior, the use of qualitative vision modules and a pointable multiresolution sensor. TEA1 demonstrates that Bayes nets and decision theoretic techniques provide a general, reusable framework for constructi...
Detecting Deception in Reputation Management
, 2003
"... We previously developed a social mechanism for distributed reputation management, in which an agent combines testimonies from several witnesses to determine its ratings of another agent. However, that approach does not fully protect against spurious ratings generated by malicious agents. This paper ..."
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Cited by 97 (3 self)
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We previously developed a social mechanism for distributed reputation management, in which an agent combines testimonies from several witnesses to determine its ratings of another agent. However, that approach does not fully protect against spurious ratings generated by malicious agents. This paper focuses on the problem of deception in testimony propagation and aggregation. We introduce some models of deception and study how to efficiently detect deceptive agents following those models. Our approach involves a novel application of the wellknown weighted majority technique to belief function and their aggregation. We describe simulation experiments to study the number of apparently accurate witnesses found in different settings, the number of witnesses on prediction accuracy, and the evolution of trust networks.
Perspectives on the Theory and Practice of Belief Functions
 International Journal of Approximate Reasoning
, 1990
"... The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answer ..."
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Cited by 86 (7 self)
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The theory of belief functions provides one way to use mathematical probability in subjective judgment. It is a generalization of the Bayesian theory of subjective probability. When we use the Bayesian theory to quantify judgments about a question, we must assign probabilities to the possible answers to that question. The theory of belief functions is more flexible; it allows us to derive degrees of belief for a question from probabilities for a related question. These degrees of belief may or may not have the mathematical properties of probabilities; how much they differ from probabilities will depend on how closely the two questions are related. Examples of what we would now call belieffunction reasoning can be found in the late seventeenth and early eighteenth centuries, well before Bayesian ideas were developed. In 1689, George Hooper gave rules for combining testimony that can be recognized as special cases of Dempster's rule for combining belief functions (Shafer 1986a). Similar rules were formulated by Jakob Bernoulli in his Ars Conjectandi, published posthumously in 1713, and by JohannHeinrich Lambert in his Neues Organon, published in 1764 (Shafer 1978). Examples of belieffunction reasoning can also be found in more recent work, by authors
Two views of belief: Belief as generalized probability and belief as evidence
, 1992
"... : Belief functions are mathematical objects defined to satisfy three axioms that look somewhat similar to the Kolmogorov axioms defining probability functions. We argue that there are (at least) two useful and quite different ways of understanding belief functions. The first is as a generalized prob ..."
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Cited by 72 (12 self)
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: Belief functions are mathematical objects defined to satisfy three axioms that look somewhat similar to the Kolmogorov axioms defining probability functions. We argue that there are (at least) two useful and quite different ways of understanding belief functions. The first is as a generalized probability function (which technically corresponds to the inner measure induced by a probability function). The second is as a way of representing evidence. Evidence, in turn, can be understood as a mapping from probability functions to probability functions. It makes sense to think of updating a belief if we think of it as a generalized probability. On the other hand, it makes sense to combine two beliefs (using, say, Dempster's rule of combination) only if we think of the belief functions as representing evidence. Many previous papers have pointed out problems with the belief function approach; the claim of this paper is that these problems can be explained as a consequence of confounding the...
Distributed Reputation Management For Electronic Commerce
, 2002
"... This paper considers the problem of automatically collecting ratings about a given party from others. Our approach involves a distributed agent architecture and adapts the mathematical theory of evidence to represent and propagate the ratings that participants give to each other. When evaluating ..."
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Cited by 57 (4 self)
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This paper considers the problem of automatically collecting ratings about a given party from others. Our approach involves a distributed agent architecture and adapts the mathematical theory of evidence to represent and propagate the ratings that participants give to each other. When evaluating the trustworthiness of a given party, a peer combines its local evidence (based on direct prior interactions with the party) with the testimonies of others regarding the same party. This approach satisfies certain important properties of distributed reputation management and is experimentally evaluated through simulations
A New Approach to Updating Beliefs
 Uncertainty in Artificial Intelligence
, 1991
"... : We define a new notion of conditional belief, which plays the same role for DempsterShafer belief functions as conditional probability does for probability functions. Our definition is different from the standard definition given by Dempster, and avoids many of the wellknown problems of that def ..."
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Cited by 47 (6 self)
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: We define a new notion of conditional belief, which plays the same role for DempsterShafer belief functions as conditional probability does for probability functions. Our definition is different from the standard definition given by Dempster, and avoids many of the wellknown problems of that definition. Just as the conditional probability P r(\DeltajB) is a probability function which is the result of conditioning on B being true, so too our conditional belief function Bel(\DeltajB) is a belief function which is the result of conditioning on B being true. We define the conditional belief as the lower envelope (that is, the inf) of a family of conditional probability functions, and provide a closedform expression for it. An alternate way of understanding our definition of conditional belief is provided by considering ideas from an earlier paper [FH91], where we connect belief functions with inner measures. In particular, we show here how to extend the definition of conditional pro...
Uncertainty, Belief, and Probability
 Computational Intelligence
, 1989
"... : We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and ..."
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Cited by 46 (2 self)
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: We introduce a new probabilistic approach to dealing with uncertainty, based on the observation that probability theory does not require that every event be assigned a probability. For a nonmeasurable event (one to which we do not assign a probability), we can talk about only the inner measure and outer measure of the event. In addition to removing the requirement that every event be assigned a probability, our approach circumvents other criticisms of probabilitybased approaches to uncertainty. For example, the measure of belief in an event turns out to be represented by an interval (defined by the inner and outer measure), rather than by a single number. Further, this approach allows us to assign a belief (inner measure) to an event E without committing to a belief about its negation :E (since the inner measure of an event plus the inner measure of its negation is not necessarily one). Interestingly enough, inner measures induced by probability measures turn out to correspond in a ...
DempsterShafer Theory for Sensor Fusion in Autonomous Mobile Robots
 IEEE Transactions on Robotics and Automation
"... This article presents the uncertainty management system used for the execution activity of the Sensor Fusion Effects (SFX) architecture. The SFX architecture is a generic sensor fusion system for autonomous mobile robots, suitable for a wide variety of sensors and environments. The execution acti ..."
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Cited by 45 (5 self)
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This article presents the uncertainty management system used for the execution activity of the Sensor Fusion Effects (SFX) architecture. The SFX architecture is a generic sensor fusion system for autonomous mobile robots, suitable for a wide variety of sensors and environments. The execution activity uses the belief generated for a percept to either proceed with a task safely (e.g., navigate to a specific location), terminate the task (e.g., can't recognize the location), or investigate the situation further in the hopes of obtaining sufficient belief (e.g., what has changed?). DempsterShafer (DS) theory serves as the foundation for uncertainty management. The SFX implementation of DS theory incorporates evidence from sensor observations and domain knowledge into three levels of perceptual abstraction. It also makes use of the DS weight of conflict metric to prevent the robot from acting on faulty observations. Experiments with four types of sensor data collected by a mobil...
Localized Partial Evaluation of Belief Networks
, 1995
"... Most algorithms for propagating evidence through belief networks have been exact and exhaustive: they produce an exact (pointvalued) marginal probability for every node in the network. Often, however, an application will not need information about every node in the network nor will it need exact pr ..."
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Cited by 43 (1 self)
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Most algorithms for propagating evidence through belief networks have been exact and exhaustive: they produce an exact (pointvalued) marginal probability for every node in the network. Often, however, an application will not need information about every node in the network nor will it need exact probabilities. We present the localized partial evaluation (LPE) propagation algorithm, which computes interval bounds on the marginal probability of a specified query node by examining a subset of the nodes in the entire network. Conceptually, LPE ignores parts of the network that are "too far away" from the queried node to have much impact on its value. LPE has the "anytime" property of being able to produce better solutions (tighter intervals) given more time to consider more of the network. 1 Introduction Belief networks provide a way of encoding knowledge about the probabilistic dependencies and independencies of a set of variables in some domain. Variables are encoded as nodes in the ne...